The present invention relates to an expert system, and more particularly to an expert system suitable for utilization in a field in which, when sufficient descriptive information is provided for solving a problem, the amount of information to be processed is increased to retard the processing operation of the information, but the descriptive information is not used practically while when descriptive information is too small and simple since the problem cannot be analyzed sufficiently and the descriptive information is not useful practically. In the latter case, an expert named a knowledge engineer is heretofore required to adjust the level of the descriptive information so as to balance the processing speed and the solving capability of the problem.
In order to improve efficiency of development of the expert system, many methods of learning and preparing technical knowledge using a computer have been proposed. Specifically, in recent years, a method named the explanation based learning has been further studied. For example, Tom M. Mitchell, Richard M. Keller and Smadar T. Kedar-Cabelli, "Explanation-Based Generalization: A Unifying View", Machine Learning 1986, pp. 47-80, describe a method of efficiently solving similar examples after learning by storing a series of applications of knowledge necessary for solving a specific problem.
In the prior art, the series of applications of knowledge necessary for solving a specific problem are stored, so that similar examples that follow after the initial learning can be solved efficiently. However, the prior art has a problem that the problem solving efficiency after the initial learning deteriorates if a human being does not determine what knowledge is necessary for solving the specific problem and does not control to distinguish between knowledge to be learned and knowledge not to be learned.